Briefings in Bioinformatics

Article	Citations
oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data	498
NetCoMi: network construction and comparison for microbiome data in R	210
Predicting drug–disease associations through layer attention graph convolutional network	187
LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files	173
Identifying drug–target interactions based on graph convolutional network and deep neural network	163
BioGPT: generative pre-trained transformer for biomedical text generation and mining	163
MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm	151
CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice	146
Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response	143
Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities	140
AntiCP 2.0: an updated model for predicting anticancer peptides	132
AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes	126
InstaDock: A single-click graphical user interface for molecular docking-based virtual high-throughput screening	124
A deep learning method for predicting metabolite–disease associations via graph neural network	121
Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology	117
Multimodal deep learning for biomedical data fusion: a review	117
A review on drug repurposing applicable to COVID-19	112
Computational recognition of lncRNA signature of tumor-infiltrating B lymphocytes with potential implications in prognosis and immunotherapy of bladder cancer	109
Circular RNAs and complex diseases: from experimental results to computational models	107
Predicting the potential human lncRNA–miRNA interactions based on graph convolution network with conditional random field	105
Exploration of natural compounds with anti-SARS-CoV-2 activityviainhibition of SARS-CoV-2 Mpro	105
The miRNA: a small but powerful RNA for COVID-19	104
Application of deep learning methods in biological networks	104
Deep-belief network for predicting potential miRNA-disease associations	103
A roadmap for multi-omics data integration using deep learning	102

DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence	101
Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research	99
Network Pharmacology and bioinformatics analyses identify intersection genes of niacin and COVID-19 as potential therapeutic targets	95
Biological network analysis with deep learning	95
A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information	93
Systemic effects of missense mutations on SARS-CoV-2 spike glycoprotein stability and receptor-binding affinity	93
A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data	91
SSI–DDI: substructure–substructure interactions for drug–drug interaction prediction	90
A survey on computational models for predicting protein–protein interactions	89
Utilizing graph machine learning within drug discovery and development	89
Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma	87
M6A2Target: a comprehensive database for targets of m6A writers, erasers and readers	87
Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source	87
Venn diagrams in bioinformatics	86
Drug repositioning based on the heterogeneous information fusion graph convolutional network	85
An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction	85
Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19	82
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides	82
Meta-i6mA: an interspecies predictor for identifying DNAN6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework	81
Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method	80
GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels	79
Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions	79
Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization	79
DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites	75
Discovery of G-quadruplex-forming sequences in SARS-CoV-2	74
Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 gliob	74
A weighted bilinear neural collaborative filtering approach for drug repositioning	73
POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability	73
Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification	73
Anticancer peptides prediction with deep representation learning features	73
Tumor immune microenvironment lncRNAs	73
Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets	71
Multi-view Multichannel Attention Graph Convolutional Network for miRNA–disease association prediction	71
Virtual screening and molecular dynamics simulation study of plant-derived compounds to identify potential inhibitors of main protease from SARS-CoV-2	70
PharmKG: a dedicated knowledge graph benchmark for bomedical data mining	70
ggmsa: a visual exploration tool for multiple sequence alignment and associated data	70
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework	69
Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer	69
Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19	67
Semantic similarity and machine learning with ontologies	66
Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19	66
Graph representation learning in bioinformatics: trends, methods and applications	66
Text mining approaches for dealing with the rapidly expanding literature on COVID-19	65
MetaFS: Performance assessment of biomarker discovery in metaproteomics	65
Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview	65
Clinical significance and immunogenomic landscape analyses of the immune cell signature based prognostic model for patients with breast cancer	64
An in silico approach to identification, categorization and prediction of nucleic acid binding proteins	63
Interpretation of deep learning in genomics and epigenomics	63
A graph auto-encoder model for miRNA-disease associations prediction	62
GPS-Palm: a deep learning-based graphic presentation system for the prediction ofS-palmitoylation sites in proteins	61

MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction	61
Molecular design in drug discovery: a comprehensive review of deep generative models	61
Identification of miRNA–disease associations via deep forest ensemble learning based on autoencoder	61
Recent advances in biomedical literature mining	61
Network-based modeling of herb combinations in traditional Chinese medicine	60
Network-based identification genetic effect of SARS-CoV-2 infections to Idiopathic pulmonary fibrosis (IPF) patients	59
Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach	59
DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops	59
A survey on deep learning in DNA/RNA motif mining	59
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations	59
DeepDTAF: a deep learning method to predict protein–ligand binding affinity	57
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data	57
An approach for normalization and quality control for NanoString RNA expression data	57
HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks	57
An effective self-supervised framework for learning expressive molecular global representations to drug discovery	56
Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset	56
MDF-SA-DDI: predicting drug–drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism	56
Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework	55
Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein	55
Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides	54
ToxinPred2: an improved method for predicting toxicity of proteins	54
Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data	54
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning	54
ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation	54
A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2	54
A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico investigation	53
Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison	53
Deep learning meets metabolomics: a methodological perspective	53
Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients	53
Therapeutic targets and signaling mechanisms of vitamin C activity against sepsis: a bioinformatics study	53
m6A regulator-mediated methylation modification patterns and characteristics of immunity and stemness in low-grade glioma	52
GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest	51
DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method	51
A protocol for dynamic model calibration	51
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity	50
Learning spatial structures of proteins improves protein–protein interaction prediction	50
Prediction and collection of protein–metabolite interactions	50
Computational drug repositioning based on multi-similarities bilinear matrix factorization	50
A novel antibacterial peptide recognition algorithm based on BERT	50
Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment	50
Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design	50
Pharmacometabonomics: data processing and statistical analysis	50
Deep drug-target binding affinity prediction with multiple attention blocks	50
Using deep neural networks and biological subwords to detect protein S-sulfenylation sites	49
Comparative analysis of molecular fingerprints in prediction of drug combination effects	49
A heterogeneous network embedding framework for predicting similarity-based drug-target interactions	49
Integrative pharmacological mechanism of vitamin C combined with glycyrrhizic acid against COVID-19: findings of bioinformatics analyses	49
FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network	49
Improving cancer driver gene identification using multi-task learning on graph convolutional network	48
Large-scale benchmark study of survival prediction methods using multi-omics data	48
Deep-DRM: a computational method for identifying disease-related metabolites based on graph deep learning approaches	48
Machine learning approach to gene essentiality prediction: a review	47
Machine learning meets omics: applications and perspectives	47
Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning	47
Cell–cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies	47
Predicting metabolite–disease associations based on auto-encoder and non-negative matrix factorization	47
HVIDB: a comprehensive database for human–virus protein–protein interactions	47
Artificial intelligence in drug discovery: applications and techniques	47
A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells	46
AlphaFold2-aware protein–DNA binding site prediction using graph transformer	46
CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer	46
DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach	46
Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions	46
Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma	46
Ferroptosis-related lncRNA pairs to predict the clinical outcome and molecular characteristics of pancreatic ductal adenocarcinoma	45
Machine learning methods, databases and tools for drug combination prediction	45
Transcriptional landscape of cholangiocarcinoma revealed by weighted gene coexpression network analysis	45
iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network	45
Predicting potential small molecule–miRNA associations based on bounded nuclear norm regularization	44
ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism	44
Deep learning in retrosynthesis planning: datasets, models and tools	44
Enriching contextualized language model from knowledge graph for biomedical information extraction	44
Accurate prediction of inter-protein residue–residue contacts for homo-oligomeric protein complexes	44
MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph	43
Multi-omics approaches for revealing the complexity of cardiovascular disease	43
ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides	43
Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors	43
DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor	43
Unsupervised and self-supervised deep learning approaches for biomedical text mining	43
Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec	43

Epidemiological data analysis of viral quasispecies in the next-generation sequencing era	43
Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations	43
Drug–drug interaction prediction with learnable size-adaptive molecular substructures	43
DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences	43
Evaluating the state of the art in missing data imputation for clinical data	42
A simple guide to de novo transcriptome assembly and annotation	41
A comprehensive overview and critical evaluation of gene regulatory network inference technologies	41
LSTM-PHV: prediction of human-virus protein–protein interactions by LSTM with word2vec	41
Recent advances in network-based methods for disease gene prediction	41
Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term	41
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction	41
SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes	41
Predicting human microbe–disease associations via graph attention networks with inductive matrix completion	40
FitDock: protein–ligand docking by template fitting	40
NPI-GNN: Predicting ncRNA–protein interactions with deep graph neural networks	40
Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA–disease association prediction	40
Transcriptome analysis of cepharanthine against a SARS-CoV-2-related coronavirus	39
Benchmarking variant callers in next-generation and third-generation sequencing analysis	39
Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification	39
DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy	39
Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks	39
Recent advances in user-friendly computational tools to engineer protein function	38
Accurate and fast cell marker gene identification with COSG	38
Identification of drug–target interactions via multiple kernel-based triple collaborative matrix factorization	38
Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings	38
Computational resources for identifying and describing proteins driving liquid–liquid phase separation	38
Proper imputation of missing values in proteomics datasets for differential expression analysis	38
AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug–drug interaction prediction	38
Integrated unsupervised–supervised modeling and prediction of protein–peptide affinities at structural level	38
Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models	37
PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques	37
iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types	37
ConSIG: consistent discovery of molecular signature from OMIC data	37
Drug–target interaction predication via multi-channel graph neural networks	37
Accurate protein function prediction via graph attention networks with predicted structure information	37
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective	37
FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction	37
Computational methods for the integrative analysis of single-cell data	37
Protein–RNA interaction prediction with deep learning: structure matters	37
Bioinformatics resources for SARS-CoV-2 discovery and surveillance	37
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data	37
Immune infiltration and clinical significance analyses of the coagulation-related genes in hepatocellular carcinoma	36
A cross-study analysis of drug response prediction in cancer cell lines	36
Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing	36
Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine	36
FireProtASR: A Web Server for Fully Automated Ancestral Sequence Reconstruction	36
A network embedding framework based on integrating multiplex network for drug combination prediction	36
Porpoise: a new approach for accurate prediction of RNA pseudouridine sites	36
Identification and characterization of circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-2	36
Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules	36
Protein design via deep learning	35
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding	35
A comprehensive survey on computational methods of non-coding RNA and disease association prediction	35
RNA–RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection	35
fastDRH: a webserver to predict and analyze protein–ligand complexes based on molecular docking and MM/PB(GB)SA computation	35
Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes	35
ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm	35
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome	35
NmRF: identification of multispecies RNA 2’-O-methylation modification sites from RNA sequences	35
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification	35
A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma	35
AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches	35
RNMFLP: Predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation	34
Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases	34
Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities	34
Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery	34
scCancer: a package for automated processing of single-cell RNA-seq data in cancer	34
GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field	33
SGANRDA: semi-supervised generative adversarial networks for predicting circRNA–disease associations	33
Predicting drug–drug interactions by graph convolutional network with multi-kernel	33
Comprehensive assessment of cellular senescence in the tumor microenvironment	33
KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network	33
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction	33
Critical downstream analysis steps for single-cell RNA sequencing data	33
The Cellular basis of loss of smell in 2019-nCoV-infected individuals	33
Depiction of tumor stemlike features and underlying relationships with hazard immune infiltrations based on large prostate cancer cohorts	32
Accurate feature selection improves single-cell RNA-seq cell clustering	32
VirusCircBase: a database of virus circular RNAs	32
Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies	32
HCC subtypes based on the activity changes of immunologic and hallmark gene sets in tumor and nontumor tissues	32
Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients	32
Computationally predicting binding affinity in protein–ligand complexes: free energy-based simulations and machine learning-based scoring functions	32
Prediction of RNA secondary structure including pseudoknots for long sequences	32
Pathogenetic profiling of COVID-19 and SARS-like viruses	32
Topoly: Python package to analyze topology of polymers	32
Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy	32
ConsRM: collection and large-scale prediction of the evolutionarily conserved RNA methylation sites, with implications for the functional epitranscriptome	32
Integrated omics analysis reveals the alteration of gut microbe–metabolites in obese adults	32
Predicting enhancer-promoter interactions by deep learning and matching heuristic	32
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery	31
A computational toolset for rapid identification of SARS-CoV-2, other viruses and microorganisms from sequencing data	31
iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism	31
Forman persistent Ricci curvature (FPRC)-based machine learning models for protein–ligand binding affinity prediction	31
DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding	31
Iterative feature representation algorithm to improve the predictive performance of N7-methylguanosine sites	31